As health systems continue to make headway on AI implementation, they are navigating the complex challenge of getting physicians and nurses on board with adopting AI-enabled workflows. Based on conversations with physician and nursing executives during The Health Management Academy’s Spring Forums, we created a discussion guide outlining their challenges and potential solutions for getting buy-in.

Four strategies for getting your care teams on board with AI:

1. Create an open dialogue with clear messaging on the rationale of your AI use cases

• Provide clear, transparent messaging on how your organization is approaching AI implementation and the reasoning for the use cases you chose.

• Leverage your CHROs, CPEs, and CNEs to develop and execute the strategy for organizational messaging. This reinforces a system-wide approach to implementation.

• Give physicians and nurses an opportunity to share their questions and concerns, while maintaining an open dialogue as implementation continues.

• Engage physician and nurse champions early.

  • Survey results show most physicians incorporate tech when their peers do. Therefore, engaging with physicians and nurses who want to be early adopters in the pilot process and governing committees can help address the concerns of skeptical colleagues.

Peer example: One Leading Health System had their CHRO spearhead the messaging and rationale behind the org investments in AI. When they started piloting documentation and inbox messaging tools, the messaging focused on how these tools would give providers more time for the work they enjoyed—being with their patients, rather than on time-consuming administrative tasks. By positioning the new tech as an asset, care teams were more open to piloting.

2. Bring nurses to the table

• Get nursing leaders to advocate for more nursing-specific AI use cases, have nursing stakeholders included in governance, and help them make the case (as the technology is available) to encourage frontline nurses to use AI.

• Make sure frontline nurses are involved in piloting nursing-centric solutions so you can get their feedback.

Peer example: One Leading Health System incorporated auto-populating capabilities in EMR flowsheets, reducing nurses’ documentation time by 23% with nurses saying, “please never take this away, it makes our assessments so much faster to document”. By focusing on reducing time-consuming, redundant work that is unique to nursing, nurses will feel seen and valued on an organizational level and be more amenable to future changes related to AI.

3. Start with high-impact, low-risk solutions

• Begin with AI applications that have a significant positive impact but minimal risk. Gradually expanding AI adoption based on successful outcomes will build employees’ confidence using new tech.

• Don’t immediately try to increase workload (e.g., panel sizes) due to productivity improvements as this will alienate clinicians and make them not trust that time gains will significantly reduce burnout.

Peer example: One Leading Health System focused on use cases that would improve physician satisfaction and reduce their cognitive overload. Based on physicians’ feedback, they piloted solutions for their biggest stressors: documentation and in-basket messaging. Being solution oriented about their pain points, they established good will with physicians. In turn, they were receptive to AI, learned how to use it quickly, and were impressed with how it reduced the effort that normally goes into documentation and in-basket messaging.

4. Overinvest in building AI literacy through training

• Successful adoption of AI is contingent on teaching people how to use it correctly. Therefore, it is critical to invest in training to educate all employees on the best ways to use AI and on their role as stewards of data integrity.

• Educating and upskilling your clinical workforce improves the likelihood that your health system will fully realize the ROI of AI, and skepticism will be minimized because employees will understand its role in augmenting their work.

• Make sure employees have resources they can rely on beyond the training stage. This includes leveraging your industry partner to provide technical support for employees after initial adoption and incorporating AI education in professional development and onboarding curricula.

Peer example: One CPE with a documentation pilot analyzed their EMR data to see how much time physicians spent on documentation. After engaging those who had lower performance scores in one or two hours of training, their pilot scores for time savings improved by 25-50%.

For more insights and data on nurses' and physician's outlook on AI, download the PDF at the top of the page.

To learn more about AI Catalyst, click here.